RedHatAI/Mistral-7B-Instruct-v0.3-GPTQ-4bit warn
The chat template differs from its base model, which changes behavior; glitch tokens that can silently corrupt ordinary input.
Could not load this model from the Hugging Face API (private, gated, or nonexistent). Findings below are from our archive.
Scan coverageStatic battery2026-08-21Weights battery2026-08-21Behavioral batterynot rundetails
| Battery | Looks at | Status |
|---|---|---|
| Static battery | Metadata & packaging | complete 2026-08-21 |
| Weights battery | Weights forensics — no GPU, no download | complete 2026-08-2132,768-token embedding scanned · 194 undertrained · lineage consistent |
| Behavioral battery | Live-inference differentials | not run |
Ingot runs three batteries against a model. What each one checks →
Findings
Scanned 2026-08-21 · published from a community scan.
medium Chat template differs from claimed parent
The chat template does not match mistralai/Mistral-7B-Instruct-v0.3's. Template drift silently changes model behavior even when weights are identical — 37% of drifted derivatives in our census left it undisclosed. Diff the templates before deploying.
How to fixingot patch
Restore the parent's chat template in `tokenizer_config.json` — a pure metadata fix.
- Run `ingot patch <owner/model>` — the patch manifest carries the parent's template and applies it to a local copy's `tokenizer_config.json`.
- Or fix by hand: copy the `chat_template` value from the parent repo's `tokenizer_config.json` into this model's, and pin your serving stack to that file.
- If the drift was intentional (the author retrained on a new template), confirm that in the model card before "fixing" it — restoring the parent template on retrained weights changes behavior too.
medium Undertrained (glitch) token surface in vocabulary
Embedding-norm scan flagged 194 undertrained tokens (norm < 0.3× the vocabulary median of 0.174), including 10 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "<0xFA>", "<0xFB>", "<0xFC>", "<0xFD>", "<0xFE>", "<0xFF>", "iNdEx", "febbra". In models where this class was tested behaviorally, such tokens silently rewrote user input into confident, schema-valid, wrong output. These are candidates from the weights alone; behavioral confirmation requires the behavioral battery.
How to fixruntime guardweight-level
Keep the affected token strings out of the model's input — the scan-derived runtime guard carries this model's exact blocklist.
- Fetch this model's guard artifact (`/api/v1/guard/<owner>/<model>`): the confirmed corrupting tokens and the low-norm candidate list, derived from the published scan.
- Screen inbound text with it (the `@ingotai/guard` package is a reference implementation) and route flagged records to a different model or human review — verbatim-copy tasks on flagged strings are the failure mode.
- The underlying cause is undertrained embeddings in the weights; a true fix is weight-level (continued pretraining on the affected tokens) — that is not a patch, it's a training job.
info Weights consistent with claimed parent mistralai/Mistral-7B-Instruct-v0.3
Mean cosine similarity of 64 sampled token-embedding rows against mistralai/Mistral-7B-Instruct-v0.3 is 0.969 — the weights plausibly descend from the declared base (relation: unspecified).
How to fix
Fix or verify the `base_model` declaration so lineage checks can run.
- If you own the repo: correct the `base_model` field in the model card metadata to the real, public parent.
- If you don't: identify the true parent (config architecture + weight shapes narrow it fast) and re-scan with that lineage in mind.
Fix it
Some findings are metadata-level and patchable — apply the fixes to your local copy (your weights never leave your machine):
npx @ingotai/scan patch RedHatAI/Mistral-7B-Instruct-v0.3-GPTQ-4bit
Remediation guidance addresses the documented findings only. It is evidence-driven repair, not a safety certification of the model.
Fingerprint
The durable profile of this model: measured weights-and-metadata facts, rebuilt on every scan and battery run. Updated 2026-08-21.
| architecture | mistral · 32 layers · 4096-dim |
| parameters | 7249.4M |
| vocabulary | 32,768 tokens |
| license | apache-2.0 |
| serialization | safetensors |
| chat template | present · sha256:26a59556925c9873 |
| claimed lineage | mistralai/Mistral-7B-Instruct-v0.3 |
| lineage verified | consistent vs mistralai/Mistral-7B-Instruct-v0.3 — embedding-row cosine 0.969 |
| glitch-token surface | 194 undertrained candidates, 10 plain-ASCII |
Full measured fingerprint
| architectures | MistralForCausalLM |
| library | transformers |
| pipeline | text-generation |
| repo files | 9 |
| revision | ed07c8f1d2c8 |
| HF snapshot | 1.8k downloads · 25 likes · updated 2024-06-10 · captured 2026-08-21 |
| embedding tensor | model.embed_tokens.weight · F16 · 32,768×4096 |
| embedding norms | median 0.1741 · mean 0.1678 |
| lineage check | consistent — cosine 0.9687 over 64 sampled rows vs mistralai/Mistral-7B-Instruct-v0.3 |
| glitch-token samples | "<0xFA>", "<0xFB>", "<0xFC>", "<0xFD>", "<0xFE>", "<0xFF>", "iNdEx", "febbra", "NdEx", "uitgen" |
Battery runs (1)the run trace behind the findings — what each job measured
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| weights | complete | 2026-08-21 05:11 | 44s | 1 |
weights run 2026-08-21 — measurements
| probes run | glitch-norm-scan, lineage-norm-correlation |
| embedding tensor | model.embed_tokens.weight · F16 · 32,768×4096 |
| glitch surface | 194 undertrained, 10 plain-ASCII |
| lineage check | consistent — cosine 0.9687 over 64 rows vs mistralai/Mistral-7B-Instruct-v0.3 |
Verdict badge
Ship the verdict in your README — it always shows the latest published analysis:
[](https://ingot.tools/models/RedHatAI/Mistral-7B-Instruct-v0.3-GPTQ-4bit)